---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: bert-base-uncased-QnA-MLQA_Dataset
  results: []
datasets:
- mlqa
language:
- en
metrics:
- exact_match
- f1
---

# bert-base-uncased-QnA-MLQA_Dataset

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased).

## Model description

For more information on how it was created, check out the following link: https://github.com/DunnBC22/NLP_Projects/blob/main/Question%26Answer/ML%20QA/ML_QA_Question%26Answer_with_BERT.ipynb

## Intended uses & limitations

This model is intended to demonstrate my ability to solve a complex problem using technology.

## Training and evaluation data

Dataset Source: https://huggingface.co/datasets/mlqa/viewer/mlqa.en.en/test


__Histogram of Input (Both Context & Question) Lengths__
![Histogram of Input (Both Context & Question) Lengths](https://github.com/DunnBC22/NLP_Projects/raw/main/Question%26Answer/ML%20QA/Images/Histogram%20of%20Input%20Lengths.png)


__Histogram of Context Lengths__
![Histogram of Context Lengths](https://github.com/DunnBC22/NLP_Projects/raw/main/Question%26Answer/ML%20QA/Images/Histogram%20of%20Context%20Lengths.png)


__Histogram of Question Lengths__
![Histogram of Question Lengths](https://github.com/DunnBC22/NLP_Projects/raw/main/Question%26Answer/ML%20QA/Images/Histogram%20of%20Question%20Lengths.png)


## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3

### Training results

| Metric Name | Metric Value |
|:-----:|:-----:|
| Exact Match | 59.6146 |
| F1 | 73.3002 |

* All values in the above chart are rounded to the nearest ten-thousandth.

### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3